Signal propagation in neural networks

A proper account of signal propagation in neuronal networks is the key to developing a genuine understanding of information processing accomplished by the brain. The data can often be in the form of the complex activity of neuronal assemblies and their spiking patterns. Under particular circumstances, neuronal assemblies produce a coordinated synchronized activity with the profile of travelling waves. These have been shown to represent the information processing of, e.g., perceptual sensitivity. We decided to model the phenomenon of the travelling wave by using a spiking neural network model of 1mm2 of primate's early visual cortices. The modelled neural column was employed to generate synchronized activity. By interconnecting said columns, we were able to model the travelling wave. In order to describe its dynamics, we developed several event-detection algorithms to identify such a travelling wave from simulated neural columns. We discovered that methods based on the detection of local dynamics in moving windows rendered most adequately the intuitive idea of the travelling wave. After establishing our event-detection approach, we shifted the focus to causal inference. Signal propagation is commonly inferred by abstract theoretical concepts like the Granger causality or transfer entropy. In this work, we..